Sensor based systems configured to collect data about crops and plants growing outdoors, in fields, and/or other growing environments (referred to as raw data) may be capable of collecting large quantities of data. Raw data, in of itself, however, is not particularly useful. Determined insights about the crops and plants based on the raw data, on the other hand, may be of value.
Given the large quantity of raw data, it would be beneficial to automate determination of useful insights from the raw data. Automation schemes are useful to the extent that they produce accurate and reliable insights. In some embodiments, the accuracy and/or reliability of an automation scheme may depend upon large quantities of raw data that has been labeled, annotated, or otherwise identified with corresponding insight(s) that are known to be of high confidence or correct (also referred to as ground truth data or ground truthed labels). Acquiring such large quantities of ground truth data may be difficult. The available ground truth data may also suffer from inconsistency, non-uniformity, and/or other variability due to variability in the collection devices (e.g., the sensors experiencing calibration drift), changes in environmental conditions, and/or subjectivity among humans who may contribute to generation of the ground truth data. Improving the acquisition and/or quality of the ground truth data may improve the quality of one or more subsequent data generated about the crops and plants and/or uses of raw data.